Explaining the underlying causes of different tabletability classifications of binary mixtures.

Area of Science:

• Pharmaceutical Sciences
• Materials Science
• Chemical Engineering

Background:

• A tabletability classification system exists for binary mixtures.
• Previous studies applied the system but did not explain the causes of observed behaviors.

Purpose of the Study:

• Investigate the reasons behind different tabletability behaviors in binary mixtures.
• Identify component attributes influencing specific mixture behaviors.
• Develop predictive models for mixture tabletability.

Main Methods:

• Characterized binary mixtures of components with varied compressibility and compactibility.
• Compared individual component properties with observed mixture behaviors.
• Developed and validated models to predict tabletability based on component attributes.

Main Results:

• Accurate models of component compressibility and compactibility can predict mixture tabletability.
• Similar component tabletability profiles result in linear mixture behavior.
• Differences in compactibility/tabletability cause negative deviations; differences in compressibility/compactibility cause positive deviations.

Conclusions:

• Component properties (compressibility, compactibility) are key determinants of binary mixture tabletability.
• Predictive modeling based on component characteristics enhances formulation development.
• Understanding deviation causes aids in optimizing tablet manufacturing processes.